Aggregating raster data inside polygons

In a previous lesson we learned how to extract the raster values at points in the landscape
We can also extract the values inside of polygons
We’ll start by loading the sf and stars packages and the Harvard Forest soils the elevation data
But this time we’ll load a DTM file that covers the entire site

library(sf)
library(stars)
library(ggplot2)
library(dplyr)

harv_dtm <- read_stars("data/HARV/HARV_dtmFull.tif")
harv_soils <- st_read("data/HARV/harv_soils.shp")

ggplot() +
  geom_stars(data = harv_dtm) +
  geom_sf(data = harv_soils, alpha = 0)

If we want to understand whether the polygon fields are related to the raster we need to extract data about the raster within each polygon
We do this using the aggregate function just like for points
The arguments are: the raster, the vector we want to aggregate the values to, and the function we want to use to combine the different pixels
Let’s calculate the average elevation in each soil polygon

elevs_by_soil <- aggregate(harv_dtm, harv_soils, mean)

You can think of this as similar to group_by and summarize in dplyr, but spatial
The function groups the digital terrain model into groups of pixels that fall inside each soil polygon
It then calculates the mean value inside each polygon
These values are stored in

elevs_by_soil$HARV_dtmFull.tif

You can add them to our simple features object using your favorite approach
I’ll use mutate from dplyr

harv_soils <- mutate(harv_soils, elevation = elevs_by_soil$HARV_dtmFull.tif)

Once we done this then we can make maps using this information
So let’s plot the our soils map but colored by average elevation within the region

ggplot() +
  geom_sf(data = harv_soils, mapping = aes(fill = elevation)) +
  scale_fill_viridis_c()

Notes